Laser scanning is used to survey many different settings such as construction sites, historical buildings, industrial facilities or any other applicable setting. Such sceneries (settings) are commonly surveyed with 3D laser scanners with use of the time-of-flight (TOF) measuring method in order to sample a cloud of 3D points within a coordinate system. Additionally, a camera may be associated with a laser scanner and may be configured to capture images associated with the setting being scanned.
An exemplary workflow with a laser scanner comprises scanning an entire object or the interior of a building by acquiring multiple point clouds from different positions. The multiple scans are acquired independently and have to be registered to obtain an overall consistent representation. A registration of the multiple point clouds is done commonly by matching corresponding targets within the scans and thereof computing the registration transformation, or by directly minimizing the distance between the overlapping regions of the scans by an optimisation method, e.g. iterative closest point (ICP) method.
Both matching the corresponding targets on the one hand and minimizing the distance between the overlapping regions on the other hand have their drawbacks. The first method requires the one-to-one correspondence between registration targets, where the targets can be artificial, e.g. special reflective registration markers or spheres; or natural objects like landmarks, edges or corners, which are visible from different scan stations.
Setting up artificial targets is a challenging and time consuming task, because it requires planning of the arrangement and installing of these targets in order to fulfil visibility constraints (at least three markers should be visible from each setup). Using natural object targets simplifies the workflow, because no installation of targets is needed, but still requires planning in order to fulfil visibility constraints. The second method requires substantial and sufficiently structured overlap of the scans to find the global minimum of the registration optimisation. Moreover, to achieve convergence in this iterative process a good initial guess is necessary.
Thus, both methods may require advanced knowledge and experience, are time consuming and often result in more scan acquisitions than actually necessary. In addition, the registration may not be readily available in the field, because it is too time consuming or complex to perform it on the fly, especially if tens or hundreds of point clouds are acquired and registered.
Scanners known from prior art are using sensors such as 2D laser profilers, inertial measurement units (IMU), or digital compasses in order to estimate a rough initial translation and rotation between the setup positions.
However, IMU sensors quickly accumulate a drift which limits the distance between two setups. A digital compass may give wrong results in case of disturbing magnetic fields from electrical equipment. 2D profilers work well only if the displacement is taking place within a plane.
So these sensors have significant limitations in automating the registration of laser scans and often work only under specific conditions.
In EP 2 998 778, a method of combining image data with point clouds is disclosed. In this application, a surveying instrument has internal camera units with which images are recorded during the movement of the instrument between setups. As a result, such process provides an initial “guess” for relative translation and rotation between the setups. Using the “guess” as a starting point, an automatic registration is then processed.
Even though an initial guess is available, the subsequent automatic registration can fail, e.g. if the whole overlapping region between scans is represented by a planar scene. In this case, there is an ambiguity in translation relative to this plane. Another drawback of this approach is that only visual information is used. The information can be processed robustly only up to a certain degree of given limited computational power. Fast rotations or quick scene changes during the movement can break the trajectory reconstruction from the images. Therefore, the handling of the instrument is limited.
EP 3 086 283 proposes a method of combining a laser-based point cloud measured with a surveying instrument, e.g. a terrestrial laser scanner or a total station, with an image-based point cloud resulting from image data captured with an external camera unit in order to fill gaps or to acquire details in higher resolution. This method is limited by a necessary overlap between a laser scan and some images from the external camera unit.
In US 2015/160342, a method of using image data to find correspondences between point clouds is disclosed. A laser scanner is equipped with cameras and images are recorded during the movement of the instrument between setups. Landmarks in the images are matched throughout the sequence and used to find correspondence between the scans. Given these correspondences, the registration can be done automatically. One disadvantage of this method is that it needs a visual correspondence between first and second setup, i.e. a sufficient number of corresponding features have to be seen at both setups. The method also requires smooth handling, because it is based on visual information only.
In DE 10 2013 110 581, a method of using IMU data to measure the displacement between two scan setups is disclosed, where the displacement is used for registering the scans. The drawback of this method is that the displacement is based on inertial measurements, which are affected by drift.
In addition, solutions known from prior art, in particular the ones using visual methods, generally can reconstruct the scale of the trajectory and environment only poorly or not at all. As well, inertial information provides only estimation for scale with a relatively low accuracy.
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One way to locate underground utilities is to detect electromagnetic fields emitted by the nature of the utility itself. This applies to utilities having a naturally occurring electrical signal, which signal emits an electromagnetic field that is detectable above the ground, such as e.g. a live power supply line, communication cables, etc. As shown e.g. in WO 2011/104314, WO 2008/064851 or WO 2008/064852, the depth or distance to a buried utility can therein be determined according to a difference in signal strength at two or more detectors or pickups, which are located in a known spacing with respect to each other.
To detect a utility without a naturally occurring signal, (for example a wiring system of switched off street lights, unused or low-voltage communication cables, gas- or water-pipes, etc.) an artificial signal can be conducted to the utility. For example, in U.S. Pat. No. 4,438,401 a metallic utility without a naturally occurring signal is directly connected to a signal-generator. In U.S. Pat. No. 5,194,812, a hollow pipe, like a gas or water pipe is detected by introducing a conductor or sonde into it. In EP 9 166 139 or EP 2 645 133, a electrical signal is coupled into a conducting underground structure by introducing a current from an AC current-source into soil by some earth-spikes, resulting in the current to follow preferably along the conductive structure as path of least resistance through soil.
Still, detecting utilities which are not naturally carrying a detectable electrical current is bothersome, requires additional external equipment and can fail in many ways.